Working Memory and Decision-Making in a Frontoparietal Circuit Model

Aperiodic and oscillatory systems underpinning human domain-general cognition

Domain-general cognitive systems are essential for adaptive human behaviour, supporting various cognitive tasks through flexible neural mechanisms. While fMRI studies link frontoparietal network activation to increasing demands across various tasks, the electrophysiological mechanisms underlying this domain-general response to demand remain unclear. Here, we used MEG/EEG, and separated the aperiodic and oscillatory components of the signals to examine their roles in domain-general cognition across three cognitive tasks using multivariate analysis. We found that both aperiodic (broadband power, slope, and intercept) and oscillatory (theta, alpha, and beta power) components coded task demand and content across all subtasks. Aperiodic broadband power in particular strongly coded task demand, in a manner that generalised across all subtasks. Source estimation suggested that increasing cognitive demand decreased aperiodic broadband power across the brain, with the strongest modulations overlapping with the frontoparietal network. In contrast, oscillatory activity showed more localised patterns of modulation, primarily in frontal or occipital regions. These results provide insights into the electrophysiological underpinnings of human domain-general cognition, highlighting the critical role of aperiodic broadband power.

Hierarchical gradients of multiple timescales in the mammalian forebrain

Many anatomical and physiological features of cortical circuits, ranging from the biophysical properties of synapses to the connectivity patterns among different neuron types, exhibit consistent variation along the hierarchical axis from sensory to association areas. Notably, the temporal correlation of neural activity at rest, known as the intrinsic timescale, increases systematically along this hierarchy in both primates and rodents, analogous to the increasing scale and complexity of spatial receptive fields. However, how the timescales for task-related activity vary across brain regions and whether their hierarchical organization appears consistently across different mammalian species remain unexplored. Here, we show that both the intrinsic timescale and those of task-related activity follow a similar hierarchical gradient in the cortices of monkeys, rats, and mice. We also found that these timescales covary similarly in both the cortex and basal ganglia, whereas the timescales of thalamic activity are shorter than cortical timescales and do not conform to the hierarchical order predicted by their cortical projections. These results suggest that the hierarchical gradient of cortical timescales might represent a universal feature of intracortical circuits in the mammalian brain.

Effects of Ketamine on Frontoparietal Interactions in a Rule-Based Antisaccade Task in Macaque Monkeys

Cognitive control is engaged by working memory processes and high-demand situations like antisaccade, where one must suppress a prepotent response. While it is known to be supported by the frontoparietal control network, how intra- and interareal dynamics contribute to cognitive control processes remains unclear. N-Methyl-d-aspartate glutamate receptors (NMDARs) play a key role in prefrontal dynamics that support cognitive control. NMDAR antagonists, such as ketamine, are known to alter task-related prefrontal activities and impair cognitive performance. However, the role of NMDAR in cognitive control-related frontoparietal dynamics remains underexplored. Here, we simultaneously recorded local field potentials and single-unit activities from the lateral prefrontal (lPFC) and posterior parietal cortices (PPC) in two male macaque monkeys during a rule-based antisaccade task, with both rule-visible (RV) and rule-memorized (RM) conditions. In addition to altering the E/I balance in both areas, ketamine had a negative impact on rule coding in true oscillatory activities. It also reduced frontoparietal coherence in a frequency- and rule-dependent manner. Granger prediction analysis revealed that ketamine induced an overall reduction in bidirectional connectivity. Among antisaccade trials, a greater reduction in lPFC-PPC connectivity during the delay period preceded a greater delay in saccadic onset under the RM condition and a greater deficit in performance under the RV condition. Lastly, ketamine compromised rule coding in lPFC neurons in both RV and RM conditions and in PPC neurons only in the RV condition. Our findings demonstrate the utility of acute NMDAR antagonists in understanding the mechanisms through which frontoparietal dynamics support cognitive control processes.

Dysfunctional oscillatory bursting patterns underlie working memory deficits in adolescents with ADHD

Identifying neural markers of clinical symptom fluctuations is prerequisite to developing more precise brain-targeted treatments in psychiatry. We have recently shown that working memory (WM) in healthy adults is dependent on the rise and fall interplay between alpha/beta and gamma bursts within frontoparietal regions, and that deviations in these patterns lead to WM performance errors. However, it is not known whether such bursting deviations underlie clinically relevant WM-related symptoms or clinical status in individuals with WM deficits. In adolescents (n=27) with attention deficit hyperactivity disorder (ADHD), we investigated WM-related dynamics between alpha/beta and gamma bursts in relation to clinical status fluctuations. Participants repeatedly completed a visual Sternberg spatial working memory task during EEG recording as part of their participation in two research studies (n=224 person-sessions). Source localizing EEG data to each participant's structural MRI, the rate and volume of alpha, beta, and gamma bursts were examined within the dorsolateral prefrontal cortex (DLPFC) and posterior parietal cortex (PPC). Alpha/beta and gamma bursts at the DLPFC and PPC displayed complimentary roles in WM processes. Alpha/beta bursting decreased during stimuli encoding and increased during the delay, while gamma bursting was elevated during encoding and decreased during the delay. Deviations in bursting patterns were associated with WM errors and clinical symptoms. We conclude that dysfunctional alpha/beta and gamma burst dynamics within the frontoparietal region underlie both intra-individual WM performance and WM symptom fluctuations in adolescents with ADHD. Such burst dynamics reflect a novel target and biomarker for WM-related treatment development.

Unraveling the Complexity of Parkinson's Disease: Insights into Pathogenesis and Precision Interventions

Parkinson's disease (PD) is a neurodegenerative disorder characterized by dopaminergic neuron loss, leading to motor and non-motor symptoms. Early detection before symptom onset is crucial but challenging. This study presents a framework integrating circuit modeling, non-equilibrium dynamics, and optimization to understand PD pathogenesis and enable precision interventions. Neuronal firing patterns, particularly oscillatory activity, play a critical role in PD pathology. The basal ganglia network, specifically the subthalamic nucleus-external globus pallidus (STN-GPe) circuitry, exhibits abnormal activity associated with motor dysfunction. The framework leverages the non-equilibrium landscape and flux theory to identify key connections generating pathological activity, providing insights into disease progression and potential intervention points. The intricate STN-GPe interplay is highlighted, shedding light on compensatory mechanisms within this circuitry may initially counteract changes but later contribute to pathological alterations as disease progresses. The framework addresses the need for comprehensive evaluation methods to assess intervention outcomes. Cross-correlations between state variables provide superior early warning signals compared to traditional indicators relying on critical slowing down. By elucidating compensatory mechanisms and circuit dynamics, the framework contributes to improved management, early detection, risk assessment, and potential prevention/delay of PD development. This pioneering research paves the way for precision medicine in neurodegenerative disorders.

Enhancing learning experiences: EEG-based passive BCI system adapts learning speed to cognitive load in real-time, with motivation as catalyst

Computer-based learning has gained popularity in recent years, providing learners greater flexibility and freedom. However, these learning environments do not consider the learner's mental state in real-time, resulting in less optimized learning experiences. This research aimed to explore the effect on the learning experience of a novel EEG-based Brain-Computer Interface (BCI) that adjusts the speed of information presentation in real-time during a learning task according to the learner's cognitive load. We also explored how motivation moderated these effects. In accordance with three experimental groups (non-adaptive, adaptive, and adaptive with motivation), participants performed a calibration task (n-back), followed by a memory-based learning task concerning astrological constellations. Learning gains were assessed based on performance on the learning task. Self-perceived mental workload, cognitive absorption and satisfaction were assessed using a post-test questionnaire. Between-group analyses using Mann-Whitney tests suggested that combining BCI and motivational factors led to more significant learning gains and an improved learning experience. No significant difference existed between the BCI without motivational factor and regular non-adaptive interface for overall learning gains, self-perceived mental workload, and cognitive absorption. However, participants who undertook the experiment with an imposed learning pace reported higher overall satisfaction with their learning experience and a higher level of temporal stress. Our findings suggest BCI's potential applicability and feasibility in improving memorization-based learning experiences. Further work should seek to optimize the BCI adaptive index and explore generalizability to other learning contexts.

Neural correlates of harm avoidance: a multimodal meta-analysis of brain structural and resting-state functional neuroimaging studies

Harm avoidance (HA) is a Cloninger personality trait that describes behavioural inhibition to avoid aversive stimuli. It serves as a predisposing factor that contributes to the development of mental disorders such as anxiety and major depressive disorder. Neuroimaging research has identified some brain anatomical and functional correlates of HA, but reported findings are inconsistent. We therefore conducted a multimodal meta-analysis of whole-brain structural and resting-state functional neuroimaging studies to identify the most stable neural substrate of HA. Included were a total of 10 structural voxel-based morphometry studies (11 datasets) and 13 functional positron emission tomography or single photon emission computed tomography studies (16 datasets) involving 3053 healthy participants without any psychiatric or neurological disorders evaluated for HA using the Three-Dimensional Personality Questionnaire (TPQ) or the Temperament and Character Inventory (TCI). The meta-analysis revealed brain volumetric correlates of HA in parietal and temporal cortices, and resting-state functional correlates in prefrontal, temporal and parietal gray matter. Volumetric and functional correlates co-occurred in the left superior frontal gyrus and left middle frontal gyrus, and were dissociated in the left rectus gyrus. Our meta-analysis is the first study to give a comprehensive picture of the structural and functional correlates of HA, a contribution that may help bridge the grievous gap between the neurobiology of HA and the pathogenesis, prevention and treatment of HA-related mental disorders.

Timescales of learning in prefrontal cortex

The lateral prefrontal cortex (PFC) in humans and other primates is critical for immediate, goal-directed behaviour and working memory, which are classically considered distinct from the cognitive and neural circuits that support long-term learning and memory. Over the past few years, a reconsideration of this textbook perspective has emerged, in that different timescales of memory-guided behaviour are in constant interaction during the pursuit of immediate goals. Here, we will first detail how neural activity related to the shortest timescales of goal-directed behaviour (which requires maintenance of current states and goals in working memory) is sculpted by long-term knowledge and learning - that is, how the past informs present behaviour. Then, we will outline how learning across different timescales (from seconds to years) drives plasticity in the primate lateral PFC, from single neuron firing rates to mesoscale neuroimaging activity patterns. Finally, we will review how, over days and months of learning, dense local and long-range connectivity patterns in PFC facilitate longer-lasting changes in population activity by changing synaptic weights and recruiting additional neural resources to inform future behaviour. Our Review sheds light on how the machinery of plasticity in PFC circuits facilitates the integration of learned experiences across time to best guide adaptive behaviour.

Maintenance of delay-period activity in working memory task is modulated by local network structure

Revealing the relationship between neural network structure and function is one central theme of neuroscience. In the context of working memory (WM), anatomical data suggested that the topological structure of microcircuits within WM gradient network may differ, and the impact of such structural heterogeneity on WM activity remains unknown. Here, we proposed a spiking neural network model that can replicate the fundamental characteristics of WM: delay-period neural activity involves association cortex but not sensory cortex. First, experimentally observed receptor expression gradient along the WM gradient network is reproduced by our network model. Second, by analyzing the correlation between different local structures and duration of WM activity, we demonstrated that small-worldness, excitation-inhibition balance, and cycle structures play crucial roles in sustaining WM-related activity. To elucidate the relationship between the structure and functionality of neural networks, structural circuit gradients in brain should also be subject to further measurement. Finally, combining anatomical data, we simulated the duration of WM activity across different brain regions, its maintenance relies on the interaction between local and distributed networks. Overall, network structural gradient and interaction between local and distributed networks are of great significance for WM.

Dissociable functional responses along the posterior-anterior gradient of the frontal and parietal cortices revealed by parametric working memory and training

While the storage capacity is limited, accumulating studies have indicated that working memory (WM) can be improved by cognitive training. However, understanding how exactly the brain copes with limited WM capacity and how cognitive training optimizes the brain remains inconclusive. Given the hierarchical functional organization of WM, we hypothesized that the activation profiles along the posterior-anterior gradient of the frontal and parietal cortices characterize WM load and training effects. To test this hypothesis, we recruited 51 healthy volunteers and adopted a parametric WM paradigm and training method. In contrast to exclusively strengthening the activation of posterior areas, a broader range of activation concurrently occurred in the anterior areas to cope with increased memory load for all subjects at baseline. Moreover, there was an imbalance in the responses of the posterior and anterior areas to the same increment of 1 item at different load levels. Although a general decrease in activation after adaptive training, the changes in the posterior and anterior areas were distinct at different memory loads. Particularly, we found that the activation gradient between the posterior and anterior areas was significantly increased at load 4-back after adaptive training, and the changes were correlated with improvement in WM performance. Together, our results demonstrate a shift in the predominant role of posterior and anterior areas in the frontal and parietal cortices when approaching WM capacity limits. Additionally, the training-induced performance improvement likely benefits from the elevated neural efficiency reflected in the increased activation gradient between the posterior and anterior areas.

Hierarchical gradients of multiple timescales in the mammalian forebrain

Many anatomical and physiological features of cortical circuits, ranging from the biophysical properties of synapses to the connectivity patterns among different neuron types, exhibit consistent variation along the hierarchical axis from sensory to association areas. Notably, the scale of temporal correlation of neural activity at rest, known as the intrinsic timescale, increases systematically along this hierarchy in both primates and rodents, analogous to the growing scale and complexity of spatial receptive fields. However, how the timescales for task-related activity vary across brain regions and whether their hierarchical organization appears consistently across different mammalian species remain unexplored. Here, we show that both the intrinsic timescale and the timescales of task-related activity follow a similar hierarchical gradient in the cortices of monkeys, rats, and mice. We also found that these timescales covary similarly in both the cortex and basal ganglia, whereas the timescales of thalamic activity are shorter than cortical timescales and do not conform to the hierarchical order predicted by their cortical projections. These results suggest that the hierarchical gradient of cortical timescales might be a universal feature of intra-cortical circuits in the mammalian brain.

A multi-region recurrent circuit for evidence accumulation in rats

Decision-making based on noisy evidence requires accumulating evidence and categorizing it to form a choice. Here we evaluate a proposed feedforward and modular mapping of this process in rats: evidence accumulated in anterodorsal striatum (ADS) is categorized in prefrontal cortex (frontal orienting fields, FOF). Contrary to this, we show that both regions appear to be indistinguishable in their encoding/decoding of accumulator value and communicate this information bidirectionally. Consistent with a role for FOF in accumulation, silencing FOF to ADS projections impacted behavior throughout the accumulation period, even while nonselective FOF silencing did not. We synthesize these findings into a multi-region recurrent neural network trained with a novel approach. In-silico experiments reveal that multiple scales of recurrence in the cortico-striatal circuit rescue computation upon nonselective FOF perturbations. These results suggest that ADS and FOF accumulate evidence in a recurrent and distributed manner, yielding redundant representations and robustness to certain perturbations.

A neurocomputational model of decision and confidence in object recognition task

How does the brain process natural visual stimuli to make a decision? Imagine driving through fog. An object looms ahead. What do you do? This decision requires not only identifying the object but also choosing an action based on your decision confidence. In this circumstance, confidence is making a bridge between seeing and believing. Our study unveils how the brain processes visual information to make such decisions with an assessment of confidence, using a model inspired by the visual cortex. To computationally model the process, this study uses a spiking neural network inspired by the hierarchy of the visual cortex in mammals to investigate the dynamics of feedforward object recognition and decision-making in the brain. The model consists of two modules: a temporal dynamic object representation module and an attractor neural network-based decision-making module. Unlike traditional models, ours captures the evolution of evidence within the visual cortex, mimicking how confidence forms in the brain. This offers a more biologically plausible approach to decision-making when encountering real-world stimuli. We conducted experiments using natural stimuli and measured accuracy, reaction time, and confidence. The model's estimated confidence aligns remarkably well with human-reported confidence. Furthermore, the model can simulate the human change-of-mind phenomenon, reflecting the ongoing evaluation of evidence in the brain. Also, this finding offers decision-making and confidence encoding share the same neural circuit.

Overview on cognitive impairment in psychotic disorders: From impaired microcircuits to dysconnectivity

Schizophrenia's cognitive deficits, often overshadowed by positive symptoms, significantly contribute to the disorder's morbidity. Increasing attention highlights these deficits as reflections of neural circuit dysfunction across various cortical regions. Numerous connectivity alterations linked to cognitive symptoms in psychotic disorders have been reported, both at the macroscopic and microscopic level, emphasizing the potential role of plasticity and microcircuits impairment during development and later stages. However, the heterogeneous clinical presentation of cognitive impairment and diverse connectivity findings pose challenges in summarizing them into a cohesive picture. This review aims to synthesize major cognitive alterations, recent insights into network structural and functional connectivity changes and proposed mechanisms and microcircuit alterations underpinning these symptoms, particularly focusing on neurodevelopmental impairment, E/I balance, and sleep disturbances. Finally, we will also comment on some of the most recent and promising therapeutic approaches that aim to target these mechanisms to address cognitive symptoms. Through this comprehensive exploration, we strive to provide an updated and nuanced overview of the multiscale connectivity impairment underlying cognitive impairment in psychotic disorders.

Graded decisions in the human brain

Decision-makers objectively commit to a definitive choice, yet at the subjective level, human decisions appear to be associated with a degree of uncertainty. Whether decisions are definitive (i.e., concluding in all-or-none choices), or whether the underlying representations are graded, remains unclear. To answer this question, we recorded intracranial neural signals directly from the brain while human subjects made perceptual decisions. The recordings revealed that broadband gamma activity reflecting each individual's decision-making process, ramped up gradually while being graded by the accumulated decision evidence. Crucially, this grading effect persisted throughout the decision process without ever reaching a definite bound at the time of choice. This effect was most prominent in the parietal cortex, a brain region traditionally implicated in decision-making. These results provide neural evidence for a graded decision process in humans and an analog framework for flexible choice behavior.

The meso-connectomes of mouse, marmoset, and macaque: network organization and the emergence of higher cognition

The recent publications of the inter-areal connectomes for mouse, marmoset, and macaque cortex have allowed deeper comparisons across rodent vs. primate cortical organization. In general, these show that the mouse has very widespread, "all-to-all" inter-areal connectivity (i.e. a "highly dense" connectome in a graph theoretical framework), while primates have a more modular organization. In this review, we highlight the relevance of these differences to function, including the example of primary visual cortex (V1) which, in the mouse, is interconnected with all other areas, therefore including other primary sensory and frontal areas. We argue that this dense inter-areal connectivity benefits multimodal associations, at the cost of reduced functional segregation. Conversely, primates have expanded cortices with a modular connectivity structure, where V1 is almost exclusively interconnected with other visual cortices, themselves organized in relatively segregated streams, and hierarchically higher cortical areas such as prefrontal cortex provide top-down regulation for specifying precise information for working memory storage and manipulation. Increased complexity in cytoarchitecture, connectivity, dendritic spine density, and receptor expression additionally reveal a sharper hierarchical organization in primate cortex. Together, we argue that these primate specializations permit separable deconstruction and selective reconstruction of representations, which is essential to higher cognition.

A critical review of hot executive functioning in youth attention-deficit/hyperactivity disorder: Methodological limitations, conceptual considerations, and future directions

Hot executive functioning (EF) - EF under emotionally or motivationally salient conditions - is a putative etiology of attention-deficit/hyperactivity disorder (ADHD), disruptive behavior problems (DBPs), and their related impairments. Despite two decades of research, the present study is the first review of the construct in youth ADHD, with a particular focus on the role of task design, age, and DBPs, as well as relevant conceptual and methodological considerations. While certain hot EF tasks have been investigated extensively (e.g., choice impulsivity), substantial inconsistency in measurement of the broader construct remains, severely limiting conclusions. Future research should a) consider the extent to which various hot EF tasks relate to one another, a higher order factor, and other related constructs; b) further investigate task design, particularly the elicitation of emotion or motivation and its anticipated effect on EF; and c) incorporate multiple levels of analysis to validate similarities and differences among tasks with regard to the affective experiences and cognitive demands they elicit. With improved measurement and conceptual clarity, hot EF has potential to advance the literature on etiological pathways to ADHD, DBPs and associated impairments and, more broadly, may represent a useful tool for understanding the influence of emotion and motivation on cognition.

Wagers for work: Decomposing the costs of cognitive effort

Some aspects of cognition are more taxing than others. Accordingly, many people will avoid cognitively demanding tasks in favor of simpler alternatives. Which components of these tasks are costly, and how much, remains unknown. Here, we use a novel task design in which subjects request wages for completing cognitive tasks and a computational modeling procedure that decomposes their wages into the costs driving them. Using working memory as a test case, our approach revealed that gating new information into memory and protecting against interference are costly. Critically, other factors, like memory load, appeared less costly. Other key factors which may drive effort costs, such as error avoidance, had minimal influence on wage requests. Our approach is sensitive to individual differences, and could be used in psychiatric populations to understand the true underlying nature of apparent cognitive deficits.

Contributions of narrow- and broad-spiking prefrontal and parietal neurons on working memory tasks

Neurons that generate persistent activity in the primate dorsolateral prefrontal and posterior parietal cortex have been shown to be predictive of behavior in working memory tasks, though subtle differences between them have been observed in how information is represented. The role of different neuron types in each of these areas has not been investigated at depth. We thus compared the activity of neurons classified as narrow-spiking, putative interneurons, and broad-spiking, putative pyramidal neurons, recorded from the dorsolateral prefrontal and posterior parietal cortex of male monkeys, to analyze their role in the maintenance of working memory. Our results demonstrate that narrow-spiking neurons are active during a range of tasks and generate persistent activity during the delay period over which stimuli need to be maintained in memory. Furthermore, the activity of narrow-spiking neurons was predictive of the subject's recall no less than that of broad-spiking neurons, which are exclusively projection neurons in the cortex. Our results show that putative interneurons play an active role during the maintenance of working memory and shed light onto the fundamental neural circuits that determine subjects' memories and judgments.

The "psychiatric" neuron: the psychic neuron of the cerebral cortex, revisited

Nearly 25 years ago, Dr. Patricia Goldman-Rakic published her review paper, "The 'Psychic' Neuron of the Cerebral Cortex," outlining the circuit-level dynamics, neurotransmitter systems, and behavioral correlates of pyramidal neurons in the cerebral cortex, particularly as they relate to working memory. In the decades since the release of this paper, the existing literature and our understanding of the pyramidal neuron have increased tremendously, and research is still underway to better characterize the role of the pyramidal neuron in both healthy and psychiatric disease states. In this review, we revisit Dr. Goldman-Rakic's characterization of the pyramidal neuron, focusing on the pyramidal neurons of the prefrontal cortex (PFC) and their role in working memory. Specifically, we examine the role of PFC pyramidal neurons in the intersection of working memory and social function and describe how deficits in working memory may actually underlie the pathophysiology of social dysfunction in psychiatric disease states. We briefly describe the cortico-cortical and corticothalamic connections between the PFC and non-PFC brain regions, as well the microcircuit dynamics of the pyramidal neuron and interneurons, and the role of both these macro- and microcircuits in the maintenance of the excitatory/inhibitory balance of the cerebral cortex for working memory function. Finally, we discuss the consequences to working memory when pyramidal neurons and their circuits are dysfunctional, emphasizing the resulting social deficits in psychiatric disease states with known working memory dysfunction.

The Associative Thalamus: A Switchboard for Cortical Operations and a Promising Target for Schizophrenia

Schizophrenia is a brain disorder that profoundly perturbs cognitive processing. Despite the success in treating many of its symptoms, the field lacks effective methods to measure and address its impact on reasoning, inference, and decision making. Prefrontal cortical abnormalities have been well documented in schizophrenia, but additional dysfunction in the interactions between the prefrontal cortex and thalamus have recently been described. This dysfunction may be interpreted in light of parallel advances in neural circuit research based on nonhuman animals, which show critical thalamic roles in maintaining and switching prefrontal activity patterns in various cognitive tasks. Here, we review this basic literature and connect it to emerging innovations in clinical research. We highlight the value of focusing on associative thalamic structures not only to better understand the very nature of cognitive processing but also to leverage these circuits for diagnostic and therapeutic development in schizophrenia. We suggest that the time is right for building close bridges between basic thalamic research and its clinical translation, particularly in the domain of cognition and schizophrenia.

What is it like to do a visuo-spatial working memory task: A qualitative phenomenological study of the visual span task

Working memory is typically measured with specifically designed psychological tasks. When evaluating the validity of working memory tasks, we commonly focus on the reliability of the outcome measurements. Only rarely do we focus on how participants experience these tasks. Accounting for lived experience of working memory task may help us better understand variability in working memory performance and conscious experience in general. We replicated recently established protocols for the phenomenological investigation of working memory using the visual span task. We collected subjective reports from eighteen healthy participants (10 women) aged 21 to 35 years. We observed that working memory can be phenomenologically characterized at three different time scales: background feelings, strategies, and tactics. On the level of tactics, we identified transmodality (i.e., how one modality of lived experience can be transformed into another one) as the central phenomenological dynamic at play during working memory task performance.

Mixed Selectivity Coding of Content-Temporal Detail by Dorsomedial Posterior Parietal Neurons

The dorsomedial posterior parietal cortex (dmPPC) is part of a higher-cognition network implicated in elaborate processes underpinning memory formation, recollection, episode reconstruction, and temporal information processing. Neural coding for complex episodic processing is however under-documented. Here, we recorded extracellular neural activities from three male rhesus macaques (Macaca mulatta) and revealed a set of neural codes of "neuroethogram" in the primate parietal cortex. Analyzing neural responses in macaque dmPPC to naturalistic videos, we discovered several groups of neurons that are sensitive to different categories of ethogram items, low-level sensory features, and saccadic eye movement. We also discovered that the processing of category and feature information by these neurons is sustained by the accumulation of temporal information over a long timescale of up to 30 s, corroborating its reported long temporal receptive windows. We performed an additional behavioral experiment with additional two male rhesus macaques and found that saccade-related activities could not account for the mixed neuronal responses elicited by the video stimuli. We further observed monkeys' scan paths and gaze consistency are modulated by video content. Taken altogether, these neural findings explain how dmPPC weaves fabrics of ongoing experiences together in real time. The high dimensionality of neural representations should motivate us to shift the focus of attention from pure selectivity neurons to mixed selectivity neurons, especially in increasingly complex naturalistic task designs.

Cell type-specific connectome predicts distributed working memory activity in the mouse brain

Recent advances in connectomics and neurophysiology make it possible to probe whole-brain mechanisms of cognition and behavior. We developed a large-scale model of the multiregional mouse brain for a cardinal cognitive function called working memory, the brain's ability to internally hold and process information without sensory input. The model is built on mesoscopic connectome data for interareal cortical connections and endowed with a macroscopic gradient of measured parvalbumin-expressing interneuron density. We found that working memory coding is distributed yet exhibits modularity; the spatial pattern of mnemonic representation is determined by long-range cell type-specific targeting and density of cell classes. Cell type-specific graph measures predict the activity patterns and a core subnetwork for memory maintenance. The model shows numerous attractor states, which are self-sustained internal states (each engaging a distinct subset of areas). This work provides a framework to interpret large-scale recordings of brain activity during cognition, while highlighting the need for cell type-specific connectomics.

The parietal cortex has a causal role in ambiguity computations in humans

Humans often face the challenge of making decisions between ambiguous options. The level of ambiguity in decision-making has been linked to activity in the parietal cortex, but its exact computational role remains elusive. To test the hypothesis that the parietal cortex plays a causal role in computing ambiguous probabilities, we conducted consecutive fMRI and TMS-EEG studies. We found that participants assigned unknown probabilities to objective probabilities, elevating the uncertainty of their decisions. Parietal cortex activity correlated with the objective degree of ambiguity and with a process that underestimates the uncertainty during decision-making. Conversely, the midcingulate cortex (MCC) encodes prediction errors and increases its connectivity with the parietal cortex during outcome processing. Disruption of the parietal activity increased the uncertainty evaluation of the options, decreasing cingulate cortex oscillations during outcome evaluation and lateral frontal oscillations related to value ambiguous probability. These results provide evidence for a causal role of the parietal cortex in computing uncertainty during ambiguous decisions made by humans.

Controlling brain dynamics: Landscape and transition path for working memory

Understanding the underlying dynamical mechanisms of the brain and controlling it is a crucial issue in brain science. The energy landscape and transition path approach provides a possible route to address these challenges. Here, taking working memory as an example, we quantified its landscape based on a large-scale macaque model. The working memory function is governed by the change of landscape and brain-wide state switching in response to the task demands. The kinetic transition path reveals that information flow follows the direction of hierarchical structure. Importantly, we propose a landscape control approach to manipulate brain state transition by modulating external stimulation or inter-areal connectivity, demonstrating the crucial roles of associative areas, especially prefrontal and parietal cortical areas in working memory performance. Our findings provide new insights into the dynamical mechanism of cognitive function, and the landscape control approach helps to develop therapeutic strategies for brain disorders.

Different resting membrane potentials in posterior parietal cortex and prefrontal cortex in the view of recurrent synaptic strengths and neural network dynamics

In this study, we introduce the importance of elevated membrane potentials (MPs) in the prefrontal cortex (PFC) compared to that in the posterior parietal cortex (PPC), based on new observations of different MP levels in these areas. Through experimental data and spiking neural network modeling, we investigated a possible mechanism of the elevated membrane potential in the PFC and how these physiological differences affect neural network dynamics and cognitive functions in the PPC and PFC. Our findings indicate that NMDA receptors may be a main contributor to the elevated MP in the PFC region and highlight the potential of using a modeling toolkit to investigate the means by which changes in synaptic properties can affect neural dynamics and potentiate desirable cognitive functions through population activities in the corresponding brain regions.

Dual-functional network regulation underlies the central executive system in working memory

The frontoparietal network (FPN) and cingulo-opercular network (CON) may exert top-down regulation corresponding to the central executive system (CES) in working memory (WM); however, contributions and regulatory mechanisms remain unclear. We examined network interaction mechanisms underpinning the CES by depicting CON- and FPN-mediated whole-brain information flow in WM. We used datasets from participants performing verbal and spatial working memory tasks, divided into encoding, maintenance, and probe stages. We used general linear models to obtain task-activated CON and FPN nodes to define regions of interest (ROI); an online meta-analysis defined alternative ROIs for validation. We calculated whole-brain functional connectivity (FC) maps seeded by CON and FPN nodes at each stage using beta sequence analysis. We used Granger causality analysis to obtain the connectivity maps and assess task-level information flow patterns. For verbal working memory, the CON functionally connected positively and negatively to task-dependent and task-independent networks, respectively, at all stages. FPN FC patterns were similar only in the encoding and maintenance stages. The CON elicited stronger task-level outputs. Main effects were: stable CON→FPN, CON→DMN, CON→visual areas, FPN→visual areas, and phonological areas→FPN. The CON and FPN both up-regulated task-dependent and down-regulated task-independent networks during encoding and probing. Task-level output was slightly stronger for the CON. CON→FPN, CON→DMN, visual areas→CON, and visual areas→FPN showed consistent effects. The CON and FPN might together underlie the CES's neural basis and achieve top-down regulation through information interaction with other large-scale functional networks, and the CON may be a higher-level regulatory core in WM.

Learning how network structure shapes decision-making for bio-inspired computing

To better understand how network structure shapes intelligent behavior, we developed a learning algorithm that we used to build personalized brain network models for 650 Human Connectome Project participants. We found that participants with higher intelligence scores took more time to solve difficult problems, and that slower solvers had higher average functional connectivity. With simulations we identified a mechanistic link between functional connectivity, intelligence, processing speed and brain synchrony for trading accuracy with speed in dependence of excitation-inhibition balance. Reduced synchrony led decision-making circuits to quickly jump to conclusions, while higher synchrony allowed for better integration of evidence and more robust working memory. Strict tests were applied to ensure reproducibility and generality of the obtained results. Here, we identify links between brain structure and function that enable to learn connectome topology from noninvasive recordings and map it to inter-individual differences in behavior, suggesting broad utility for research and clinical applications.

Concurrent- and After-Effects of Medial Temporal Lobe Stimulation on Directed Information Flow to and from Prefrontal and Parietal Cortices during Memory Formation

Electrical stimulation of the medial temporal lobe (MTL) has the potential to uncover causal circuit mechanisms underlying memory function. However, little is known about how MTL stimulation alters information flow with frontoparietal cortical regions implicated in episodic memory. We used intracranial EEG recordings from humans (14 participants, 10 females) to investigate how MTL stimulation alters directed information flow between MTL and PFC and between MTL and posterior parietal cortex (PPC). Participants performed a verbal episodic memory task during which they were presented with words and asked to recall them after a delay of ∼20 s; 50 Hz stimulation was applied to MTL electrodes on selected trials during memory encoding. Directed information flow was examined using phase transfer entropy. Behaviorally, we observed that MTL stimulation reduced memory recall. MTL stimulation decreased top-down PFC→MTL directed information flow during both memory encoding and subsequent memory recall, revealing aftereffects more than 20 s after end of stimulation. Stimulation suppressed top-down PFC→MTL influences to a greater extent than PPC→MTL. Finally, MTL→PFC information flow on stimulation trials was significantly lower for successful, compared with unsuccessful, memory recall; in contrast, MTL→ventral PPC information flow was higher for successful, compared with unsuccessful, memory recall. Together, these results demonstrate that the effects of MTL stimulation are behaviorally, regionally, and directionally specific, that MTL stimulation selectively impairs directional signaling with PFC, and that causal MTL-ventral PPC circuits support successful memory recall. Findings provide new insights into dynamic casual circuits underling episodic memory and their modulation by MTL stimulation.SIGNIFICANCE STATEMENT The medial temporal lobe (MTL) and its interactions with prefrontal and parietal cortices (PFC and PPC) play a critical role in human memory. Dysfunctional MTL-PFC and MTL-PPC circuits are prominent in psychiatric and neurologic disorders, including Alzheimer's disease and schizophrenia. Brain stimulation has emerged as a potential mechanism for enhancing memory and cognitive functions, but the underlying neurophysiological mechanisms and dynamic causal circuitry underlying bottom-up and top-down signaling involving the MTL are unknown. Here, we use intracranial EEG recordings to investigate the effects of MTL stimulation on causal signaling in key episodic memory circuits linking the MTL with PFC and PPC. Our findings have implications for translational applications aimed at realizing the promise of brain stimulation-based treatment of memory disorders.

The Entangled Brain

The Entangled Brain (Pessoa, L., 2002. MIT Press) promotes the idea that we need to understand the brain as a complex, entangled system. Why does the complex systems perspective, one that entails emergent properties, matter for brain science? In fact, many neuroscientists consider these ideas a distraction. We discuss three principles of brain organization that inform the question of the interactional complexity of the brain: (1) massive combinatorial anatomical connectivity; (2) highly distributed functional coordination; and (3) networks/circuits as functional units. To motivate the challenges of mapping structure and function, we discuss neural circuits illustrating the high anatomical and functional interactional complexity typical in the brain. We discuss potential avenues for testing for network-level properties, including those relying on distributed computations across multiple regions. We discuss implications for brain science, including the need to characterize decentralized and heterarchical anatomical-functional organization. The view advocated has important implications for causation, too, because traditional accounts of causality provide poor candidates for explanation in interactionally complex systems like the brain given the distributed, mutual, and reciprocal nature of the interactions. Ultimately, to make progress understanding how the brain supports complex mental functions, we need to dissolve boundaries within the brain-those suggested to be associated with perception, cognition, action, emotion, motivation-as well as outside the brain, as we bring down the walls between biology, psychology, mathematics, computer science, philosophy, and so on.

Predicted utility modulates working memory fidelity in the brain

The predicted utility of information stored in working memory (WM) is hypothesized to influence the strategic allocation of WM resources. Prior work has shown that when information is prioritized, it is remembered with greater precision relative to other remembered items. However, these paradigms often complicate interpretation of the effects of predicted utility on item fidelity due to a concurrent memory load. Likewise, no fMRI studies have examined whether the predicted utility of an item modulates fidelity in the neural representation of items during the memory delay without a concurrent load. In the current study, we used fMRI to investigate whether predicted utility influences fidelity of WM representations in the brain. Using a generative model multivoxel analysis approach to estimate the quality of remembered representations across predicted utility conditions, we observed that items with greater predicted utility are maintained in memory with greater fidelity, even when they are the only item being maintained. Further, we found that this pattern follows a parametric relationship where more predicted utility corresponded to greater fidelity. These precision differences could not be accounted for based on a redistribution of resources among already-remembered items. Rather, we interpret these results in terms of a gating mechanism that allows for pre-allocation of resources based on predicted value alone. This evidence supports a theoretical distinction between resource allocation that occurs as a result of load and resource pre-allocation that occurs as a result of predicted utility.

Low-dimensional encoding of decisions in parietal cortex reflects long-term training history

Neurons in parietal cortex exhibit task-related activity during decision-making tasks. However, it remains unclear how long-term training to perform different tasks over months or even years shapes neural computations and representations. We examine lateral intraparietal area (LIP) responses during a visual motion delayed-match-to-category task. We consider two pairs of male macaque monkeys with different training histories: one trained only on the categorization task, and another first trained to perform fine motion-direction discrimination (i.e., pretrained). We introduce a novel analytical approach-generalized multilinear models-to quantify low-dimensional, task-relevant components in population activity. During the categorization task, we found stronger cosine-like motion-direction tuning in the pretrained monkeys than in the category-only monkeys, and that the pretrained monkeys' performance depended more heavily on fine discrimination between sample and test stimuli. These results suggest that sensory representations in LIP depend on the sequence of tasks that the animals have learned, underscoring the importance of considering training history in studies with complex behavioral tasks.

Residual dynamics resolves recurrent contributions to neural computation

Relating neural activity to behavior requires an understanding of how neural computations arise from the coordinated dynamics of distributed, recurrently connected neural populations. However, inferring the nature of recurrent dynamics from partial recordings of a neural circuit presents considerable challenges. Here we show that some of these challenges can be overcome by a fine-grained analysis of the dynamics of neural residuals-that is, trial-by-trial variability around the mean neural population trajectory for a given task condition. Residual dynamics in macaque prefrontal cortex (PFC) in a saccade-based perceptual decision-making task reveals recurrent dynamics that is time dependent, but consistently stable, and suggests that pronounced rotational structure in PFC trajectories during saccades is driven by inputs from upstream areas. The properties of residual dynamics restrict the possible contributions of PFC to decision-making and saccade generation and suggest a path toward fully characterizing distributed neural computations with large-scale neural recordings and targeted causal perturbations.

Microfoundations of Adaptive Search in Complex Tasks: The Role of Cognitive Abilities and Styles

Problem-solving in complex environments requires a cognitively demanding search for task solutions. Managing this search process presents a major challenge in organizations. We contribute to the literature on this topic by providing new evidence on the cognitive antecedents that shape how individuals search when engaged in complex problem-solving tasks. We present results from three laboratory studies, wherein 335 individuals solved a complex task. In doing so, they generated behavioral data coupled with survey-based measurements of the individuals’ cognitive styles and performance-based tests of their cognitive abilities. Our data analysis contributes to the current literature by documenting systematic heterogeneity in the persistence and distance of search that can be explained by the participants’ level of creativity, attention to detail, and executive functions. We extend the research on the microfoundations of adaptive search by linking cognitive antecedents with a complex search task, widening our insight into what search behavior certain cognitive microfoundations lead to, and showing how managers can more effectively shape organizational search.

History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.1654 .

Explaining Integration of Evidence Separated by Temporal Gaps with Frontoparietal Circuit Models

Perceptual decisions rely on accumulating sensory evidence over time. However, the accumulation process is complicated in real life when evidence resulted from separated cues over time. Previous studies demonstrate that participants are able to integrate information from two separated cues to improve their performance invariant to an interval between the cues. However, there is no neural model that can account for accuracy and confidence in decisions when there is a time interval in evidence. We used behavioral and EEG datasets from a visual choice task -Random dot motion- with separated evidence to investigate three candid distributed neural networks. We showed that decisions based on evidence accumulation by separated cues over time are best explained by the interplay of recurrent cortical dynamics of centro-parietal and frontal brain areas while an uncertainty-monitoring module included in the model.

Reduced working memory performance in PTSD and suicide among veterans presenting for treatment

Suicide is among the leading causes of death in the United States, underscoring the continued need to understand the mechanisms underlying suicide risk. A growing body of research has examined the role of working memory deficits in suicidal thoughts and behaviors (STBs), yet little research has evaluated putative pathways via which working memory impairments may heighten suicide risk. Elevated posttraumatic stress symptoms (PTSS) represent one plausible mechanism through which poor working memory performance may increase STBs. The present study utilized data from 140 treatment-seeking veterans who presented for an intake evaluation in the PTSD Clinical Team of a large VA Medical Center. Veterans completed self-report measures, a semi-structured PTSD evaluation, and a digit span working memory test. In addition to concurrent suicidal ideation assessed during the intake, additional information regarding past suicide attempts, presence of a safety plan, documentation of past suicidal behaviors, and engagement with suicide crisis lines were collected via electronic medical records. Consistent with hypotheses, a significant indirect path emerged such that poor working memory performance predicted greater suicidal ideation, greater likelihood of a past suicide attempt, and greater latent suicide risk via increased PTSS. However, no direct effect of working memory on STBs or indirect paths of PTSS on STBs via working memory emerged. These findings suggest that the relation between working memory and STBs may be explained by PTSS severity.

A role for the claustrum in cognitive control

Early hypotheses of claustrum function were fueled by neuroanatomical data and yielded suggestions that the claustrum is involved in processes ranging from salience detection to multisensory integration for perceptual binding. While these hypotheses spurred useful investigations, incompatibilities inherent in these views must be reconciled to further conceptualize claustrum function amid a wealth of new data. Here, we review the varied models of claustrum function and synthesize them with developments in the field to produce a novel functional model: network instantiation in cognitive control (NICC). This model proposes that frontal cortices direct the claustrum to flexibly instantiate cortical networks to subserve cognitive control. We present literature support for this model and provide testable predictions arising from this conceptual framework.

Flexible integration of continuous sensory evidence in perceptual estimation tasks

Temporal accumulation of evidence is crucial for making accurate judgments based on noisy or ambiguous sensory input. The integration process leading to categorical decisions is thought to rely on competition between neural populations, each encoding a discrete categorical choice. How recurrent neural circuits integrate evidence for continuous perceptual judgments is unknown. Here, we show that a continuous bump attractor network can integrate a circular feature, such as stimulus direction, nearly optimally. As required by optimal integration, the population activity of the network unfolds on a two-dimensional manifold, in which the position of the network's activity bump tracks the stimulus average, and, simultaneously, the bump amplitude tracks stimulus uncertainty. Moreover, the temporal weighting of sensory evidence by the network depends on the relative strength of the stimulus compared to the internally generated bump dynamics, yielding either early (primacy), uniform, or late (recency) weighting. The model can flexibly switch between these regimes by changing a single control parameter, the global excitatory drive. We show that this mechanism can quantitatively explain individual temporal weighting profiles of human observers, and we validate the model prediction that temporal weighting impacts reaction times. Our findings point to continuous attractor dynamics as a plausible neural mechanism underlying stimulus integration in perceptual estimation tasks.

Sex Differences in Cognition in Schizophrenia: What We Know and What We Do Not Know

Cognitive impairment is a core feature of schizophrenia. This selective review examines whether schizophrenia patients show preserved sexual dimorphism in cognition. Existing studies using performance tasks largely show comparable sex effects between schizophrenia patients and healthy populations. This pattern appears to be similar across multiple cognitive domains and across phase of illness. Our selective review also identifies several unresolved questions about sex differences in cognition in schizophrenia. A better understanding of sex differences in cognition in schizophrenia may provide important clues to probing the relationship between cognitive impairment and pathophysiological processes of the disorder.

Functional topologies of spatial cognition predict cognitive and motor progression in Parkinson’s

Background

Spatial cognition deteriorates in Parkinson’s disease (PD), but the neural substrates are not understood, despite the risk for future dementia. It is also unclear whether deteriorating spatial cognition relates to changes in other cognitive domains or contributes to motor dysfunction.

Objective

This study aimed to identify functional connectivity abnormalities in cognitively normal PD (PDCN) in regions that support spatial cognition to determine their relationship to interfacing cognitive functions and motor disability, and to determine if they predict cognitive and motor progression 2 years later in a PDCN subsample.

Methods

Sixty-three PDCN and 43 controls underwent functional MRI while judging whether pictures, rotated at various angles, depicted the left or right hand. The task activates systems that respond to increases in rotation angle, a proxy for visuospatial difficulty. Angle-modulated functional connectivity was analyzed for frontal cortex, posterior cortex, and basal ganglia regions.

Results

Two aberrant connectivity patterns were found in PDCN, which were condensed into principal components that characterized the strength and topology of angle-modulated connectivity. One topology related to a marked failure to amplify frontal, posterior, and basal ganglia connectivity with other brain areas as visuospatial demands increased, unlike the control group (control features). Another topology related to functional reorganization whereby regional connectivity was strengthened with brain areas not recruited by the control group (PDCN features). Functional topologies correlated with diverse cognitive domains at baseline, underscoring their influences on spatial cognition. In PDCN, expression of topologies that were control features predicted greater cognitive progression longitudinally, suggesting inefficient communications within circuitry normally recruited to handle spatial demands. Conversely, stronger expression of topologies that were PDCN features predicted less longitudinal cognitive decline, suggesting functional reorganization was compensatory. Parieto-occipital topologies (control features) had different prognostic implications for longitudinal changes in motor disability. Expression of one topology predicted less motor decline, whereas expression of another predicted increased postural instability and gait disturbance (PIGD) feature severity. Concurrently, greater longitudinal decline in spatial cognition predicted greater motor and PIGD feature progression, suggesting deterioration in shared substrates.

Conclusion

These novel discoveries elucidate functional mechanisms of visuospatial cognition in PDCN, which foreshadow future cognitive and motor disability.

Uncertainty modulates visual maps during noninstrumental information demand

Animals are intrinsically motivated to obtain information independently of instrumental incentives. This motivation depends on two factors: a desire to resolve uncertainty by gathering accurate information and a desire to obtain positively-valenced observations, which predict favorable rather than unfavorable outcomes. To understand the neural mechanisms, we recorded parietal cortical activity implicated in prioritizing stimuli for spatial attention and gaze, in a task in which monkeys were free (but not trained) to obtain information about probabilistic non-contingent rewards. We show that valence and uncertainty independently modulated parietal neuronal activity, and uncertainty but not reward-related enhancement consistently correlated with behavioral sensitivity. The findings suggest uncertainty-driven and valence-driven information demand depend on partially distinct pathways, with the former being consistently related to parietal responses and the latter depending on additional mechanisms implemented in downstream structures.

Modulation of working memory duration by synaptic and astrocytic mechanisms

Short-term synaptic plasticity and modulations of the presynaptic vesicle release rate are key components of many working memory models. At the same time, an increasing number of studies suggests a potential role of astrocytes in modulating higher cognitive function such as WM through their influence on synaptic transmission. Which influence astrocytic signaling could have on the stability and duration of WM representations, however, is still unclear. Here, we introduce a slow, activity-dependent astrocytic regulation of the presynaptic release probability in a synaptic attractor model of WM. We compare and analyze simulations of a simple WM protocol in firing rate and spiking networks with and without astrocytic regulation, and underpin our observations with analyses of the phase space dynamics in the rate network. We find that the duration and stability of working memory representations are altered by astrocytic signaling and by noise. We show that astrocytic signaling modulates the mean duration of WM representations. Moreover, if the astrocytic regulation is strong, a slow presynaptic timescale introduces a 'window of vulnerability', during which WM representations are easily disruptable by noise before being stabilized. We identify two mechanisms through which noise from different sources in the network can either stabilize or destabilize WM representations. Our findings suggest that (i) astrocytic regulation can act as a crucial determinant for the duration of WM representations in synaptic attractor models of WM, and (ii) that astrocytic signaling could facilitate different mechanisms for volitional top-down control of WM representations and their duration.

Oscillatory beta/alpha band modulations: A potential biomarker of functional language and motor recovery in chronic stroke?

Stroke remains one of the leading causes of various disabilities, including debilitating motor and language impairments. Though various treatments exist, post-stroke impairments frequently become chronic, dramatically reducing daily life quality, and requiring specific rehabilitation. A critical goal of chronic stroke rehabilitation is to induce, usually through behavioral training, experience-dependent plasticity processes in order to promote functional recovery. However, the efficiency of such interventions is typically modest, and very little is known regarding the neural dynamics underpinning recovery processes and possible biomarkers of their efficiency. Some studies have emphasized specific alterations of excitatory–inhibitory balance within distributed neural networks as an important recovery correlate. Neural processes sensitive to these alterations, such as task-dependent oscillatory activity in beta as well as alpha bands, may be candidate biomarkers of chronic stroke functional recovery. In this review, we discuss the results of studies on motor and language recovery with a focus on oscillatory processes centered around the beta band and their modulations during functional recovery in chronic stroke. The discussion is based on a framework where task-dependent modulations of beta and alpha oscillatory activity, generated by the deep cortical excitatory–inhibitory microcircuits, serve as a neural mechanism of domain-general top-down control processes. We discuss the findings, their limitations, and possible directions for future research.

Practice-related changes in perceptual evidence accumulation correlate with changes in working memory

It has been proposed that evidence accumulation determines not only the speed and accuracy of simple perceptual decisions but also influences performance on tasks assessing higher-order cognitive abilities, such as working memory (WM). Accordingly, estimates of evidence accumulation based on diffusion decision modeling of perceptual decision-making tasks have been found to correlate with WM performance. Here we use diffusion decision modeling in combination with latent factor modeling to test the stronger prediction that practice-induced changes in evidence accumulation correlate with changes in WM performance. Analyses are based on data from the COGITO Study, in which 101 young adults practiced a battery of cognitive tasks, including three simple two-choice reaction time tasks and three WM tasks, in 100 day-to-day training sessions distributed over 6 months. In initial analyses, drift rates were found to correlate across the three choice tasks, such that latent factors of evidence accumulation could be established. These latent factors of evidence accumulation were positively correlated with latent factors of practiced and unpracticed WM tasks, both before and after practice. As predicted, individual differences in changes of evidence accumulation correlated positively with changes in WM performance. Our findings support the proposition that decision making and WM both rely on the active maintenance of task-relevant internal representations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Adolescent-specific memory effects: evidence from working memory, immediate and long-term recognition memory performance in 8-30 yr olds

Working memory and recognition memory develop across adolescence, but the relationship between them is not fully understood. We investigated associations between n-back task performance and subsequent recognition memory in a community sample (8-30 yr, n = 150) using tasks from the Adolescent Brain Cognitive Development Study (ABCD Study) to cross-sectionally assess memory in an age range that will be sampled longitudinally. We added a 24-h delay condition to assess long-term recognition. Overall working memory, immediate and long-term recognition performance peaked in adolescence. Age effects in recognition memory varied by items (old targets, old distractors, and new items) and delay (0 and 24 h). For immediate recognition, accuracy was higher for targets and new items than for distractors, with accuracy for targets peaking in adulthood and accuracy for new items peaking during adolescence. For long-term recognition, adolescents' accuracy was higher for targets than distractors, while adults showed similarly high accuracy for targets and distractors and children showed low accuracy for both. This pattern appeared to be specific to recognition of items from the high working memory load condition. The results suggest that working memory may facilitate long-term recognition of task-relevant over irrelevant items and may benefit the detection of new information during adolescence.

Cortical feedback loops bind distributed representations of working memory

Working memory—the brain’s ability to internalize information and use it flexibly to guide behaviour—is an essential component of cognition. Although activity related to working memory has been observed in several brain regions^1–3, how neural populations actually represent working memory^4–7 and the mechanisms by which this activity is maintained^8–12 remain unclear^13–15. Here we describe the neural implementation of visual working memory in mice alternating between a delayed non-match-to-sample task and a simple discrimination task that does not require working memory but has identical stimulus, movement and reward statistics. Transient optogenetic inactivations revealed that distributed areas of the neocortex were required selectively for the maintenance of working memory. Population activity in visual area AM and premotor area M2 during the delay period was dominated by orderly low-dimensional dynamics^16,17 that were, however, independent of working memory. Instead, working memory representations were embedded in high-dimensional population activity, present in both cortical areas, persisted throughout the inter-stimulus delay period, and predicted behavioural responses during the working memory task. To test whether the distributed nature of working memory was dependent on reciprocal interactions between cortical regions^18–20, we silenced one cortical area (AM or M2) while recording the feedback it received from the other. Transient inactivation of either area led to the selective disruption of inter-areal communication of working memory. Therefore, reciprocally interconnected cortical areas maintain bound high-dimensional representations of working memory.

Experiments in mice alternating between a visual working memory task and a task that is independent of working memory provide insight into the neural representation of working memory and the distributed nature of its maintenance.

Individual differences in frontoparietal plasticity in humans

Neuroplasticity, defined as the brain's potential to change in response to its environment, has been extensively studied at the cellular and molecular levels. Work in animal models suggests that stimulation to the ventral tegmental area (VTA) enhances plasticity, and that myelination constrains plasticity. Little is known, however, about whether proxy measures of these properties in the human brain are associated with learning. Here, we investigated the plasticity of the frontoparietal system by asking whether VTA resting-state functional connectivity and myelin map values (T1w/T2w ratios) predicted learning after short-term training on the adaptive n-back (n = 46, ages 18-25). We found that stronger baseline connectivity between VTA and lateral prefrontal cortex predicted greater improvements in accuracy. Lower myelin map values predicted improvements in response times, but not accuracy. Our findings suggest that proxy markers of neural plasticity can predict learning in humans.

Extracting Dynamical Understanding From Neural-Mass Models of Mouse Cortex

New brain atlases with high spatial resolution and whole-brain coverage have rapidly advanced our knowledge of the brain's neural architecture, including the systematic variation of excitatory and inhibitory cell densities across the mammalian cortex. But understanding how the brain's microscale physiology shapes brain dynamics at the macroscale has remained a challenge. While physiologically based mathematical models of brain dynamics are well placed to bridge this explanatory gap, their complexity can form a barrier to providing clear mechanistic interpretation of the dynamics they generate. In this work, we develop a neural-mass model of the mouse cortex and show how bifurcation diagrams, which capture local dynamical responses to inputs and their variation across brain regions, can be used to understand the resulting whole-brain dynamics. We show that strong fits to resting-state functional magnetic resonance imaging (fMRI) data can be found in surprisingly simple dynamical regimes-including where all brain regions are confined to a stable fixed point-in which regions are able to respond strongly to variations in their inputs, consistent with direct structural connections providing a strong constraint on functional connectivity in the anesthetized mouse. We also use bifurcation diagrams to show how perturbations to local excitatory and inhibitory coupling strengths across the cortex, constrained by cell-density data, provide spatially dependent constraints on resulting cortical activity, and support a greater diversity of coincident dynamical regimes. Our work illustrates methods for visualizing and interpreting model performance in terms of underlying dynamical mechanisms, an approach that is crucial for building explanatory and physiologically grounded models of the dynamical principles that underpin large-scale brain activity.

Metastable dynamics of neural circuits and networks

Cortical neurons emit seemingly erratic trains of action potentials or "spikes," and neural network dynamics emerge from the coordinated spiking activity within neural circuits. These rich dynamics manifest themselves in a variety of patterns, which emerge spontaneously or in response to incoming activity produced by sensory inputs. In this Review, we focus on neural dynamics that is best understood as a sequence of repeated activations of a number of discrete hidden states. These transiently occupied states are termed "metastable" and have been linked to important sensory and cognitive functions. In the rodent gustatory cortex, for instance, metastable dynamics have been associated with stimulus coding, with states of expectation, and with decision making. In frontal, parietal, and motor areas of macaques, metastable activity has been related to behavioral performance, choice behavior, task difficulty, and attention. In this article, we review the experimental evidence for neural metastable dynamics together with theoretical approaches to the study of metastable activity in neural circuits. These approaches include (i) a theoretical framework based on non-equilibrium statistical physics for network dynamics; (ii) statistical approaches to extract information about metastable states from a variety of neural signals; and (iii) recent neural network approaches, informed by experimental results, to model the emergence of metastable dynamics. By discussing these topics, we aim to provide a cohesive view of how transitions between different states of activity may provide the neural underpinnings for essential functions such as perception, memory, expectation, or decision making, and more generally, how the study of metastable neural activity may advance our understanding of neural circuit function in health and disease.